Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations10000
Missing cells3054
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 MiB
Average record size in memory787.3 B

Variable types

Numeric9
Categorical5
Boolean7

Alerts

Alcohol Consumption has 2586 (25.9%) missing values Missing

Reproduction

Analysis started2025-01-06 00:52:51.994317
Analysis finished2025-01-06 00:53:33.747364
Duration41.75 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct63
Distinct (%)0.6%
Missing29
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean49.296259
Minimum18
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-01-06T09:53:33.859596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q134
median49
Q365
95-th percentile77
Maximum80
Range62
Interquartile range (IQR)31

Descriptive statistics

Standard deviation18.19397
Coefficient of variation (CV)0.36907405
Kurtosis-1.2036698
Mean49.296259
Median Absolute Deviation (MAD)16
Skewness-0.0067890787
Sum491533
Variance331.02055
MonotonicityNot monotonic
2025-01-06T09:53:34.041666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 187
 
1.9%
64 185
 
1.8%
34 182
 
1.8%
43 182
 
1.8%
62 181
 
1.8%
72 180
 
1.8%
66 179
 
1.8%
76 176
 
1.8%
40 174
 
1.7%
49 173
 
1.7%
Other values (53) 8172
81.7%
ValueCountFrequency (%)
18 149
1.5%
19 155
1.6%
20 154
1.5%
21 162
1.6%
22 142
1.4%
23 160
1.6%
24 132
1.3%
25 168
1.7%
26 147
1.5%
27 142
1.4%
ValueCountFrequency (%)
80 160
1.6%
79 168
1.7%
78 159
1.6%
77 169
1.7%
76 176
1.8%
75 165
1.7%
74 157
1.6%
73 152
1.5%
72 180
1.8%
71 187
1.9%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing19
Missing (%)0.2%
Memory size605.5 KiB
Male
5003 
Female
4978 

Length

Max length6
Median length4
Mean length4.9974952
Min length4

Characters and Unicode

Total characters49880
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 5003
50.0%
Female 4978
49.8%
(Missing) 19
 
0.2%

Length

2025-01-06T09:53:34.243820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-06T09:53:34.392615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
male 5003
50.1%
female 4978
49.9%

Most occurring characters

ValueCountFrequency (%)
e 14959
30.0%
a 9981
20.0%
l 9981
20.0%
M 5003
 
10.0%
F 4978
 
10.0%
m 4978
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14959
30.0%
a 9981
20.0%
l 9981
20.0%
M 5003
 
10.0%
F 4978
 
10.0%
m 4978
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14959
30.0%
a 9981
20.0%
l 9981
20.0%
M 5003
 
10.0%
F 4978
 
10.0%
m 4978
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14959
30.0%
a 9981
20.0%
l 9981
20.0%
M 5003
 
10.0%
F 4978
 
10.0%
m 4978
 
10.0%

Blood Pressure
Real number (ℝ)

Distinct61
Distinct (%)0.6%
Missing19
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean149.75774
Minimum120
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-01-06T09:53:34.542734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile123
Q1134
median150
Q3165
95-th percentile177
Maximum180
Range60
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.572969
Coefficient of variation (CV)0.11734264
Kurtosis-1.2118518
Mean149.75774
Median Absolute Deviation (MAD)15
Skewness0.013907286
Sum1494732
Variance308.80924
MonotonicityNot monotonic
2025-01-06T09:53:34.759345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134 214
 
2.1%
167 195
 
1.9%
171 182
 
1.8%
142 181
 
1.8%
140 181
 
1.8%
170 181
 
1.8%
126 178
 
1.8%
133 178
 
1.8%
129 177
 
1.8%
136 177
 
1.8%
Other values (51) 8137
81.4%
ValueCountFrequency (%)
120 174
1.7%
121 162
1.6%
122 161
1.6%
123 161
1.6%
124 169
1.7%
125 148
1.5%
126 178
1.8%
127 169
1.7%
128 171
1.7%
129 177
1.8%
ValueCountFrequency (%)
180 148
1.5%
179 140
1.4%
178 155
1.6%
177 172
1.7%
176 143
1.4%
175 167
1.7%
174 149
1.5%
173 167
1.7%
172 165
1.7%
171 182
1.8%

Cholesterol Level
Real number (ℝ)

Distinct151
Distinct (%)1.5%
Missing30
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean225.42558
Minimum150
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-01-06T09:53:34.962079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile158
Q1187
median226
Q3263
95-th percentile293
Maximum300
Range150
Interquartile range (IQR)76

Descriptive statistics

Standard deviation43.575809
Coefficient of variation (CV)0.19330464
Kurtosis-1.2051322
Mean225.42558
Median Absolute Deviation (MAD)38
Skewness-0.0071201575
Sum2247493
Variance1898.8512
MonotonicityNot monotonic
2025-01-06T09:53:35.159186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
292 91
 
0.9%
186 84
 
0.8%
288 83
 
0.8%
185 83
 
0.8%
193 81
 
0.8%
255 79
 
0.8%
165 78
 
0.8%
162 78
 
0.8%
289 77
 
0.8%
166 77
 
0.8%
Other values (141) 9159
91.6%
ValueCountFrequency (%)
150 61
0.6%
151 57
0.6%
152 72
0.7%
153 62
0.6%
154 65
0.7%
155 66
0.7%
156 62
0.6%
157 49
0.5%
158 61
0.6%
159 66
0.7%
ValueCountFrequency (%)
300 68
0.7%
299 71
0.7%
298 51
0.5%
297 60
0.6%
296 70
0.7%
295 62
0.6%
294 67
0.7%
293 66
0.7%
292 91
0.9%
291 55
0.5%

Exercise Habits
Categorical

Distinct3
Distinct (%)< 0.1%
Missing25
Missing (%)0.2%
Memory size599.0 KiB
High
3372 
Medium
3332 
Low
3271 

Length

Max length6
Median length4
Mean length4.3401504
Min length3

Characters and Unicode

Total characters43293
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowLow
4th rowHigh
5th rowLow

Common Values

ValueCountFrequency (%)
High 3372
33.7%
Medium 3332
33.3%
Low 3271
32.7%
(Missing) 25
 
0.2%

Length

2025-01-06T09:53:35.410113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-06T09:53:35.572444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
high 3372
33.8%
medium 3332
33.4%
low 3271
32.8%

Most occurring characters

ValueCountFrequency (%)
i 6704
15.5%
H 3372
7.8%
g 3372
7.8%
h 3372
7.8%
M 3332
7.7%
e 3332
7.7%
d 3332
7.7%
u 3332
7.7%
m 3332
7.7%
L 3271
7.6%
Other values (2) 6542
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 6704
15.5%
H 3372
7.8%
g 3372
7.8%
h 3372
7.8%
M 3332
7.7%
e 3332
7.7%
d 3332
7.7%
u 3332
7.7%
m 3332
7.7%
L 3271
7.6%
Other values (2) 6542
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 6704
15.5%
H 3372
7.8%
g 3372
7.8%
h 3372
7.8%
M 3332
7.7%
e 3332
7.7%
d 3332
7.7%
u 3332
7.7%
m 3332
7.7%
L 3271
7.6%
Other values (2) 6542
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 6704
15.5%
H 3372
7.8%
g 3372
7.8%
h 3372
7.8%
M 3332
7.7%
e 3332
7.7%
d 3332
7.7%
u 3332
7.7%
m 3332
7.7%
L 3271
7.6%
Other values (2) 6542
15.1%

Smoking
Boolean

Distinct2
Distinct (%)< 0.1%
Missing25
Missing (%)0.2%
Memory size19.7 KiB
True
5123 
False
4852 
(Missing)
 
25
ValueCountFrequency (%)
True 5123
51.2%
False 4852
48.5%
(Missing) 25
 
0.2%
2025-01-06T09:53:35.699915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing21
Missing (%)0.2%
Memory size19.7 KiB
False
5004 
True
4975 
(Missing)
 
21
ValueCountFrequency (%)
False 5004
50.0%
True 4975
49.8%
(Missing) 21
 
0.2%
2025-01-06T09:53:35.817600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Diabetes
Boolean

Distinct2
Distinct (%)< 0.1%
Missing30
Missing (%)0.3%
Memory size19.7 KiB
False
5018 
True
4952 
(Missing)
 
30
ValueCountFrequency (%)
False 5018
50.2%
True 4952
49.5%
(Missing) 30
 
0.3%
2025-01-06T09:53:35.925326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

BMI
Real number (ℝ)

Distinct9978
Distinct (%)100.0%
Missing22
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean29.077269
Minimum18.002837
Maximum39.996954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-01-06T09:53:36.059653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum18.002837
5-th percentile19.142354
Q123.658075
median29.079492
Q334.520015
95-th percentile38.875096
Maximum39.996954
Range21.994117
Interquartile range (IQR)10.86194

Descriptive statistics

Standard deviation6.3070982
Coefficient of variation (CV)0.2169082
Kurtosis-1.1814578
Mean29.077269
Median Absolute Deviation (MAD)5.4295222
Skewness-0.021342412
Sum290132.99
Variance39.779487
MonotonicityNot monotonic
2025-01-06T09:53:36.280716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.99159109 1
 
< 0.1%
18.05695757 1
 
< 0.1%
28.73666675 1
 
< 0.1%
27.84084544 1
 
< 0.1%
23.68829856 1
 
< 0.1%
27.81803869 1
 
< 0.1%
22.47766533 1
 
< 0.1%
33.62495301 1
 
< 0.1%
37.99327298 1
 
< 0.1%
30.23764133 1
 
< 0.1%
Other values (9968) 9968
99.7%
(Missing) 22
 
0.2%
ValueCountFrequency (%)
18.00283694 1
< 0.1%
18.0070582 1
< 0.1%
18.00870862 1
< 0.1%
18.00875696 1
< 0.1%
18.00891781 1
< 0.1%
18.01162775 1
< 0.1%
18.01450821 1
< 0.1%
18.0147327 1
< 0.1%
18.01572549 1
< 0.1%
18.01600423 1
< 0.1%
ValueCountFrequency (%)
39.9969538 1
< 0.1%
39.99579742 1
< 0.1%
39.99459813 1
< 0.1%
39.98949339 1
< 0.1%
39.98901483 1
< 0.1%
39.9888456 1
< 0.1%
39.97948634 1
< 0.1%
39.97591451 1
< 0.1%
39.97447577 1
< 0.1%
39.97063009 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing26
Missing (%)0.3%
Memory size19.7 KiB
True
5022 
False
4952 
(Missing)
 
26
ValueCountFrequency (%)
True 5022
50.2%
False 4952
49.5%
(Missing) 26
 
0.3%
2025-01-06T09:53:36.406858image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing25
Missing (%)0.2%
Memory size19.7 KiB
True
5000 
False
4975 
(Missing)
 
25
ValueCountFrequency (%)
True 5000
50.0%
False 4975
49.8%
(Missing) 25
 
0.2%
2025-01-06T09:53:36.512047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing26
Missing (%)0.3%
Memory size19.7 KiB
False
5036 
True
4938 
(Missing)
 
26
ValueCountFrequency (%)
False 5036
50.4%
True 4938
49.4%
(Missing) 26
 
0.3%
2025-01-06T09:53:36.613509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Alcohol Consumption
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing2586
Missing (%)25.9%
Memory size585.7 KiB
Medium
2500 
Low
2488 
High
2426 

Length

Max length6
Median length4
Mean length4.3388185
Min length3

Characters and Unicode

Total characters32168
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowMedium
3rd rowLow
4th rowLow
5th rowLow

Common Values

ValueCountFrequency (%)
Medium 2500
25.0%
Low 2488
24.9%
High 2426
24.3%
(Missing) 2586
25.9%

Length

2025-01-06T09:53:36.744587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-06T09:53:36.875785image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
medium 2500
33.7%
low 2488
33.6%
high 2426
32.7%

Most occurring characters

ValueCountFrequency (%)
i 4926
15.3%
M 2500
7.8%
e 2500
7.8%
d 2500
7.8%
u 2500
7.8%
m 2500
7.8%
L 2488
7.7%
o 2488
7.7%
w 2488
7.7%
H 2426
7.5%
Other values (2) 4852
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4926
15.3%
M 2500
7.8%
e 2500
7.8%
d 2500
7.8%
u 2500
7.8%
m 2500
7.8%
L 2488
7.7%
o 2488
7.7%
w 2488
7.7%
H 2426
7.5%
Other values (2) 4852
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4926
15.3%
M 2500
7.8%
e 2500
7.8%
d 2500
7.8%
u 2500
7.8%
m 2500
7.8%
L 2488
7.7%
o 2488
7.7%
w 2488
7.7%
H 2426
7.5%
Other values (2) 4852
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4926
15.3%
M 2500
7.8%
e 2500
7.8%
d 2500
7.8%
u 2500
7.8%
m 2500
7.8%
L 2488
7.7%
o 2488
7.7%
w 2488
7.7%
H 2426
7.5%
Other values (2) 4852
15.1%

Stress Level
Categorical

Distinct3
Distinct (%)< 0.1%
Missing22
Missing (%)0.2%
Memory size599.1 KiB
Medium
3387 
Low
3320 
High
3271 

Length

Max length6
Median length4
Mean length4.3461616
Min length3

Characters and Unicode

Total characters43366
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowHigh
3rd rowLow
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
Medium 3387
33.9%
Low 3320
33.2%
High 3271
32.7%
(Missing) 22
 
0.2%

Length

2025-01-06T09:53:37.024397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-06T09:53:37.166074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
medium 3387
33.9%
low 3320
33.3%
high 3271
32.8%

Most occurring characters

ValueCountFrequency (%)
i 6658
15.4%
M 3387
7.8%
e 3387
7.8%
d 3387
7.8%
u 3387
7.8%
m 3387
7.8%
L 3320
7.7%
o 3320
7.7%
w 3320
7.7%
H 3271
7.5%
Other values (2) 6542
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 6658
15.4%
M 3387
7.8%
e 3387
7.8%
d 3387
7.8%
u 3387
7.8%
m 3387
7.8%
L 3320
7.7%
o 3320
7.7%
w 3320
7.7%
H 3271
7.5%
Other values (2) 6542
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 6658
15.4%
M 3387
7.8%
e 3387
7.8%
d 3387
7.8%
u 3387
7.8%
m 3387
7.8%
L 3320
7.7%
o 3320
7.7%
w 3320
7.7%
H 3271
7.5%
Other values (2) 6542
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 6658
15.4%
M 3387
7.8%
e 3387
7.8%
d 3387
7.8%
u 3387
7.8%
m 3387
7.8%
L 3320
7.7%
o 3320
7.7%
w 3320
7.7%
H 3271
7.5%
Other values (2) 6542
15.1%

Sleep Hours
Real number (ℝ)

Distinct9975
Distinct (%)100.0%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean6.9913294
Minimum4.0006055
Maximum9.9999523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-01-06T09:53:37.330149image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4.0006055
5-th percentile4.2779833
Q15.4498662
median7.0032523
Q38.5315772
95-th percentile9.7062321
Maximum9.9999523
Range5.9993468
Interquartile range (IQR)3.081711

Descriptive statistics

Standard deviation1.7531952
Coefficient of variation (CV)0.25076708
Kurtosis-1.2242052
Mean6.9913294
Median Absolute Deviation (MAD)1.5386287
Skewness0.0001715222
Sum69738.511
Variance3.0736936
MonotonicityNot monotonic
2025-01-06T09:53:37.511599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.63322838 1
 
< 0.1%
6.600080829 1
 
< 0.1%
8.017111756 1
 
< 0.1%
4.55997574 1
 
< 0.1%
9.600425292 1
 
< 0.1%
4.74476551 1
 
< 0.1%
8.002935673 1
 
< 0.1%
7.810732471 1
 
< 0.1%
8.188182738 1
 
< 0.1%
6.293711264 1
 
< 0.1%
Other values (9965) 9965
99.7%
(Missing) 25
 
0.2%
ValueCountFrequency (%)
4.000605496 1
< 0.1%
4.000772948 1
< 0.1%
4.001081817 1
< 0.1%
4.00211061 1
< 0.1%
4.002624344 1
< 0.1%
4.002661697 1
< 0.1%
4.002895496 1
< 0.1%
4.003256213 1
< 0.1%
4.005054241 1
< 0.1%
4.005261905 1
< 0.1%
ValueCountFrequency (%)
9.999952254 1
< 0.1%
9.99915082 1
< 0.1%
9.998776396 1
< 0.1%
9.998525334 1
< 0.1%
9.9982211 1
< 0.1%
9.998006734 1
< 0.1%
9.997739297 1
< 0.1%
9.997299691 1
< 0.1%
9.997255758 1
< 0.1%
9.996252129 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing30
Missing (%)0.3%
Memory size598.7 KiB
Low
3390 
High
3330 
Medium
3250 

Length

Max length6
Median length4
Mean length4.3119358
Min length3

Characters and Unicode

Total characters42990
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowLow
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
Low 3390
33.9%
High 3330
33.3%
Medium 3250
32.5%
(Missing) 30
 
0.3%

Length

2025-01-06T09:53:37.695305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-06T09:53:37.994904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
low 3390
34.0%
high 3330
33.4%
medium 3250
32.6%

Most occurring characters

ValueCountFrequency (%)
i 6580
15.3%
L 3390
7.9%
o 3390
7.9%
w 3390
7.9%
H 3330
7.7%
g 3330
7.7%
h 3330
7.7%
M 3250
7.6%
e 3250
7.6%
d 3250
7.6%
Other values (2) 6500
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 6580
15.3%
L 3390
7.9%
o 3390
7.9%
w 3390
7.9%
H 3330
7.7%
g 3330
7.7%
h 3330
7.7%
M 3250
7.6%
e 3250
7.6%
d 3250
7.6%
Other values (2) 6500
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 6580
15.3%
L 3390
7.9%
o 3390
7.9%
w 3390
7.9%
H 3330
7.7%
g 3330
7.7%
h 3330
7.7%
M 3250
7.6%
e 3250
7.6%
d 3250
7.6%
Other values (2) 6500
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 6580
15.3%
L 3390
7.9%
o 3390
7.9%
w 3390
7.9%
H 3330
7.7%
g 3330
7.7%
h 3330
7.7%
M 3250
7.6%
e 3250
7.6%
d 3250
7.6%
Other values (2) 6500
15.1%

Triglyceride Level
Real number (ℝ)

Distinct301
Distinct (%)3.0%
Missing26
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean250.73441
Minimum100
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-01-06T09:53:38.148852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile114
Q1176
median250
Q3326
95-th percentile387
Maximum400
Range300
Interquartile range (IQR)150

Descriptive statistics

Standard deviation87.067226
Coefficient of variation (CV)0.34724881
Kurtosis-1.1991569
Mean250.73441
Median Absolute Deviation (MAD)75
Skewness0.0061418717
Sum2500825
Variance7580.7018
MonotonicityNot monotonic
2025-01-06T09:53:38.329293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
307 48
 
0.5%
215 47
 
0.5%
228 47
 
0.5%
349 46
 
0.5%
400 46
 
0.5%
375 46
 
0.5%
173 46
 
0.5%
251 45
 
0.4%
186 45
 
0.4%
317 44
 
0.4%
Other values (291) 9514
95.1%
ValueCountFrequency (%)
100 38
0.4%
101 32
0.3%
102 31
0.3%
103 36
0.4%
104 24
0.2%
105 33
0.3%
106 31
0.3%
107 35
0.4%
108 25
0.2%
109 34
0.3%
ValueCountFrequency (%)
400 46
0.5%
399 35
0.4%
398 33
0.3%
397 41
0.4%
396 38
0.4%
395 30
0.3%
394 41
0.4%
393 39
0.4%
392 34
0.3%
391 25
0.2%

Fasting Blood Sugar
Real number (ℝ)

Distinct81
Distinct (%)0.8%
Missing22
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean120.14221
Minimum80
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-01-06T09:53:38.523850image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile84
Q199
median120
Q3141
95-th percentile156
Maximum160
Range80
Interquartile range (IQR)42

Descriptive statistics

Standard deviation23.584011
Coefficient of variation (CV)0.19630079
Kurtosis-1.2300455
Mean120.14221
Median Absolute Deviation (MAD)21
Skewness-0.0089147155
Sum1198779
Variance556.20559
MonotonicityNot monotonic
2025-01-06T09:53:38.710024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119 151
 
1.5%
149 149
 
1.5%
148 148
 
1.5%
96 147
 
1.5%
85 141
 
1.4%
139 139
 
1.4%
153 139
 
1.4%
111 139
 
1.4%
138 138
 
1.4%
141 138
 
1.4%
Other values (71) 8549
85.5%
ValueCountFrequency (%)
80 117
1.2%
81 127
1.3%
82 119
1.2%
83 134
1.3%
84 130
1.3%
85 141
1.4%
86 98
1.0%
87 127
1.3%
88 128
1.3%
89 118
1.2%
ValueCountFrequency (%)
160 126
1.3%
159 128
1.3%
158 124
1.2%
157 116
1.2%
156 137
1.4%
155 103
1.0%
154 135
1.4%
153 139
1.4%
152 113
1.1%
151 130
1.3%

CRP Level
Real number (ℝ)

Distinct9974
Distinct (%)100.0%
Missing26
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean7.4722006
Minimum0.0036467124
Maximum14.997087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-01-06T09:53:38.895211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.0036467124
5-th percentile0.70941976
Q13.6741262
median7.4721644
Q311.255592
95-th percentile14.208761
Maximum14.997087
Range14.99344
Interquartile range (IQR)7.5814655

Descriptive statistics

Standard deviation4.3402476
Coefficient of variation (CV)0.58085266
Kurtosis-1.2093956
Mean7.4722006
Median Absolute Deviation (MAD)3.7910339
Skewness-0.0040687196
Sum74527.729
Variance18.837749
MonotonicityNot monotonic
2025-01-06T09:53:39.059346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.96924569 1
 
< 0.1%
7.82322549 1
 
< 0.1%
1.761838151 1
 
< 0.1%
5.630721103 1
 
< 0.1%
10.1942412 1
 
< 0.1%
13.84827798 1
 
< 0.1%
12.90229278 1
 
< 0.1%
2.452627325 1
 
< 0.1%
0.9317634501 1
 
< 0.1%
8.340128549 1
 
< 0.1%
Other values (9964) 9964
99.6%
(Missing) 26
 
0.3%
ValueCountFrequency (%)
0.003646712362 1
< 0.1%
0.008811190431 1
< 0.1%
0.01058465733 1
< 0.1%
0.01133684843 1
< 0.1%
0.0126981196 1
< 0.1%
0.01487135133 1
< 0.1%
0.0154669267 1
< 0.1%
0.01928247725 1
< 0.1%
0.02003004144 1
< 0.1%
0.02054069269 1
< 0.1%
ValueCountFrequency (%)
14.99708673 1
< 0.1%
14.99662473 1
< 0.1%
14.9964313 1
< 0.1%
14.9949198 1
< 0.1%
14.98959621 1
< 0.1%
14.98177895 1
< 0.1%
14.98171819 1
< 0.1%
14.97812258 1
< 0.1%
14.97652019 1
< 0.1%
14.97579734 1
< 0.1%

Homocysteine Level
Real number (ℝ)

Distinct9980
Distinct (%)100.0%
Missing20
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean12.456271
Minimum5.0002365
Maximum19.999037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-01-06T09:53:39.226110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5.0002365
5-th percentile5.7125898
Q18.7233342
median12.409395
Q316.140564
95-th percentile19.248525
Maximum19.999037
Range14.998801
Interquartile range (IQR)7.4172297

Descriptive statistics

Standard deviation4.3234259
Coefficient of variation (CV)0.3470883
Kurtosis-1.1797686
Mean12.456271
Median Absolute Deviation (MAD)3.7052369
Skewness0.0078862811
Sum124313.58
Variance18.692011
MonotonicityNot monotonic
2025-01-06T09:53:39.410659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.3872504 1
 
< 0.1%
17.69504762 1
 
< 0.1%
15.83024389 1
 
< 0.1%
5.358912056 1
 
< 0.1%
9.445296887 1
 
< 0.1%
12.72935985 1
 
< 0.1%
8.062251698 1
 
< 0.1%
11.85308719 1
 
< 0.1%
15.25711017 1
 
< 0.1%
6.149319869 1
 
< 0.1%
Other values (9970) 9970
99.7%
(Missing) 20
 
0.2%
ValueCountFrequency (%)
5.000236488 1
< 0.1%
5.001208789 1
< 0.1%
5.002809604 1
< 0.1%
5.002877423 1
< 0.1%
5.006436734 1
< 0.1%
5.009292436 1
< 0.1%
5.010650326 1
< 0.1%
5.010787553 1
< 0.1%
5.011536896 1
< 0.1%
5.011873943 1
< 0.1%
ValueCountFrequency (%)
19.99903699 1
< 0.1%
19.99875896 1
< 0.1%
19.99703165 1
< 0.1%
19.99654662 1
< 0.1%
19.99606262 1
< 0.1%
19.99591638 1
< 0.1%
19.99329153 1
< 0.1%
19.9926266 1
< 0.1%
19.99202839 1
< 0.1%
19.99164212 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
8000 
True
2000 
ValueCountFrequency (%)
False 8000
80.0%
True 2000
 
20.0%
2025-01-06T09:53:39.546733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Interactions

2025-01-06T09:53:29.688365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:52:53.975172image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:20.811760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:21.901920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:23.188139image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:24.274809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:25.833697image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:27.103028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:28.392544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:29.829167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:52:54.128000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:20.927803image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:22.012907image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:23.315702image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:24.416105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:25.989222image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:27.257761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:28.527054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:29.965054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:52:54.225621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:21.046535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:22.129765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:23.435298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:24.574360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:26.153107image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:27.407370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:28.671202image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:30.086762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:52:54.308933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:21.181659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:22.269372image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:23.554457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:24.799157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:26.306831image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:27.535815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:28.812065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:30.204713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:52:55.119956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:21.287466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:22.384134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:23.648733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:24.952242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:26.417477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:27.669391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:28.953249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:30.337299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:20.330251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:21.397970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:22.570529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:23.765464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:25.224935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:26.550179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:27.824318image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:29.114629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:30.477053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:20.449270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:21.528367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:22.720156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:23.882169image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:25.435605image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:26.691396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:27.958717image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:29.271218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:30.603799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:20.561880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:21.643915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:22.866216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:24.016930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:25.579239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:26.839845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:28.108106image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:29.423216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:30.737302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:20.678627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:21.762776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:22.995363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:24.148954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:25.720190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:26.974619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:28.237119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-06T09:53:29.553734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-06T09:53:39.646835image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
AgeAlcohol ConsumptionBMIBlood PressureCRP LevelCholesterol LevelDiabetesExercise HabitsFamily Heart DiseaseFasting Blood SugarGenderHeart Disease StatusHigh Blood PressureHigh LDL CholesterolHomocysteine LevelLow HDL CholesterolSleep HoursSmokingStress LevelSugar ConsumptionTriglyceride Level
Age1.0000.0100.011-0.0210.0090.0110.0000.0000.008-0.0060.0000.0220.0370.034-0.0070.0140.0030.0000.0190.006-0.008
Alcohol Consumption0.0101.0000.0130.0000.0140.0090.0000.0070.0070.0000.0180.0000.0000.0260.0150.0000.0000.0000.0140.0270.000
BMI0.0110.0131.0000.005-0.0170.0220.0000.0000.0070.0060.0180.0160.0130.0240.0040.029-0.0010.0290.0130.0000.005
Blood Pressure-0.0210.0000.0051.000-0.010-0.0120.0090.0000.000-0.0120.0180.0250.0090.000-0.0030.0000.0010.0160.0280.0220.008
CRP Level0.0090.014-0.017-0.0101.000-0.0180.0000.0000.0090.0100.0000.0000.0000.000-0.0100.0000.0020.0120.0000.000-0.006
Cholesterol Level0.0110.0090.022-0.012-0.0181.0000.0190.0200.0170.0000.0000.0000.0210.000-0.0060.0000.0110.0000.0000.0110.001
Diabetes0.0000.0000.0000.0090.0000.0191.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0170.0070.0230.0000.000
Exercise Habits0.0000.0070.0000.0000.0000.0200.0001.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0150.0000.0000.011
Family Heart Disease0.0080.0070.0070.0000.0090.0170.0000.0001.0000.0000.0000.0000.0150.0050.0120.0000.0210.0000.0000.0000.000
Fasting Blood Sugar-0.0060.0000.006-0.0120.0100.0000.0000.0000.0001.0000.0000.0000.0200.019-0.0200.0000.0090.0000.0000.0000.008
Gender0.0000.0180.0180.0180.0000.0000.0000.0000.0000.0001.0000.0130.0100.0040.0320.0000.0240.0030.0080.0000.029
Heart Disease Status0.0220.0000.0160.0250.0000.0000.0000.0000.0000.0000.0131.0000.0000.0000.0000.0000.0000.0000.0240.0000.000
High Blood Pressure0.0370.0000.0130.0090.0000.0210.0000.0000.0150.0200.0100.0001.0000.0000.0000.0000.0160.0150.0000.0080.000
High LDL Cholesterol0.0340.0260.0240.0000.0000.0000.0000.0160.0050.0190.0040.0000.0001.0000.0100.0000.0100.0010.0140.0000.008
Homocysteine Level-0.0070.0150.004-0.003-0.010-0.0060.0220.0000.012-0.0200.0320.0000.0000.0101.0000.000-0.0200.0180.0000.010-0.006
Low HDL Cholesterol0.0140.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0080.0000.0000.000
Sleep Hours0.0030.000-0.0010.0010.0020.0110.0170.0000.0210.0090.0240.0000.0160.010-0.0200.0001.0000.0160.0000.0000.002
Smoking0.0000.0000.0290.0160.0120.0000.0070.0150.0000.0000.0030.0000.0150.0010.0180.0080.0161.0000.0000.0000.000
Stress Level0.0190.0140.0130.0280.0000.0000.0230.0000.0000.0000.0080.0240.0000.0140.0000.0000.0000.0001.0000.0090.000
Sugar Consumption0.0060.0270.0000.0220.0000.0110.0000.0000.0000.0000.0000.0000.0080.0000.0100.0000.0000.0000.0091.0000.013
Triglyceride Level-0.0080.0000.0050.008-0.0060.0010.0000.0110.0000.0080.0290.0000.0000.008-0.0060.0000.0020.0000.0000.0131.000

Missing values

2025-01-06T09:53:30.957885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-06T09:53:31.390428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-06T09:53:33.406439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AgeGenderBlood PressureCholesterol LevelExercise HabitsSmokingFamily Heart DiseaseDiabetesBMIHigh Blood PressureLow HDL CholesterolHigh LDL CholesterolAlcohol ConsumptionStress LevelSleep HoursSugar ConsumptionTriglyceride LevelFasting Blood SugarCRP LevelHomocysteine LevelHeart Disease Status
056.00Male153.00155.00HighYesYesNo24.99YesYesNoHighMedium7.63Medium342.00NaN12.9712.39No
169.00Female146.00286.00HighNoYesYes25.22NoYesNoMediumHigh8.74Medium133.00157.009.3619.30No
246.00Male126.00216.00LowNoNoNo29.86NoYesYesLowLow4.44Low393.0092.0012.7111.23No
332.00Female122.00293.00HighYesYesNo24.13YesNoYesLowHigh5.25High293.0094.0012.515.96No
460.00Male166.00242.00LowYesYesYes20.49YesNoNoLowHigh7.03High263.00154.0010.388.15No
525.00Male152.00257.00LowYesNoNo28.14NoNoNoLowMedium5.50Low126.0091.004.3010.82No
678.00Female121.00175.00HighYesYesYes18.04NoYesNoMediumMedium9.24Medium107.0085.0011.5819.66No
738.00Female161.00187.00LowYesYesYes34.74NoNoNoLowMedium7.84High228.00111.004.9317.15No
856.00Female135.00291.00LowNoYesYes34.49YesNaNYesHighLow6.94High317.00103.005.126.05No
975.00Male144.00252.00LowYesYesNo30.14NoNoYesLowMedium4.00High199.0096.0010.017.60No
AgeGenderBlood PressureCholesterol LevelExercise HabitsSmokingFamily Heart DiseaseDiabetesBMIHigh Blood PressureLow HDL CholesterolHigh LDL CholesterolAlcohol ConsumptionStress LevelSleep HoursSugar ConsumptionTriglyceride LevelFasting Blood SugarCRP LevelHomocysteine LevelHeart Disease Status
999055.00Female131.00160.00HighYesNoYes32.36NoNoNoLowMedium5.02Medium267.00129.000.6011.30Yes
999141.00Female160.00241.00MediumNoYesYes39.35YesYesYesNaNHigh4.34Low178.00106.004.7214.22Yes
999268.00Female169.00291.00MediumYesNoNo22.84NoYesNoMediumLow6.06High299.00142.003.3211.91Yes
999327.00Female153.00188.00MediumNoNoYes28.17YesYesNoNaNMedium4.83High300.00118.006.2213.11Yes
999473.00Female144.00191.00MediumYesYesYes39.46NoNoNoMediumLow7.55Medium200.0088.001.158.02Yes
999525.00Female136.00243.00MediumYesNoNo18.79YesNoYesMediumHigh6.83Medium343.00133.003.5919.13Yes
999638.00Male172.00154.00MediumNoNoNo31.86YesNoYesNaNHigh8.25Low377.0083.002.669.72Yes
999773.00Male152.00201.00HighYesNoYes26.90NoYesYesNaNLow4.44Low248.0088.004.419.49Yes
999823.00Male142.00299.00LowYesNoYes34.96YesNoYesMediumHigh8.53Medium113.00153.007.2211.87Yes
999938.00Female128.00193.00MediumYesYesYes25.11NoYesYesHighMedium5.66High121.00149.0014.396.21Yes